This talk describes the MITRE Undersea sounding experiment (MUSE16) conducted in Narragansett Bay from September 12-23, 2016, where acoustic communication, localization waveforms, and signal processing techniques were explored. This experiment utilized newly developed acoustic buoys which were designed and built by the University of Rhode Island (URI) Ocean Engineering Dept. in collaboration with the MITRE Corporation. The buoys use Global Positioning Satellites (GPS) for localization and time synchronization and are capable of both transmitting and receiving acoustic data in the range of 8-18 kHz. The buoys were designed to further research in the areas of acoustic communications, channel modeling, and continuous active sonar (CAS). For the communication and channel modeling experimentation, modulated M-sequences of various sequence length were transmitted to explore channel characterization and communication enhancements. For the CAS experimentation, Linear Frequency Modulated (LFM) chirps of various bandwidths and center frequencies were explored as well as utilization of several underwater targets. A description of the prototype buoys including hardware, software, experimental setup, types of data collected, as well as some initial results will be discussed.
It is useful to passively characterize the underwater acoustic communications environment for a variety of purposes, including interference avoidance and enforcement of restrictions protecting marine mammals. Automatically determining the modulation of a received waveform can permit sonar or communications operations within the same bandwidth with a minimum of collisions, and it can identify a particular system operating outside its permitted regime. The characterization system needs to both determine the modulation and an unknown, time-varying channel impulse response since the transmitter and receiver are not coordinating. In this work, we demonstrate the use of blind equalization along with Convolutional Neural Networks for automatic classification of underwater signals. Our current research focuses on classification of constant modulus signals and demonstrates an approximate 30 percent improvement in modulation classification, compared to approaches without equalization, and a significant reduction in the amount of data needed for training. We considered BPSK, QPSK, MSK, FSK and 8-PSK modulations using simplified synthetic channels simulated via MATLAB to demonstrate our results. Future work is aimed at demonstrating classification improvement using realistic channel models simulated via the Sonar Simulation Toolkit, real underwater channels gathered from data collects, and additional underwater acoustic signal types.
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